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Estimation of causal effects involves crucial assumptions about thedata-generating process, such as directionality of effect, presence ofinstrumental variables or mediators, and whether all relevant confounders areobserved. Violation of any of these assumptions leads to significant error inthe effect estimate. However, unlike cross-validation for predictive models,there is no global validator method for a causal estimate. As a result,expressing different causal assumptions formally and validating them (to theextent possible) becomes critical for any analysis. We present DoWhy, aframework that allows explicit declaration of assumptions through a causalgraph and provides multiple validation tests to check a subset of theseassumptions. Our experience with DoWhy highlights a number of open questionsfor future research: developing new ways beyond causal graphs to expressassumptions, the role of causal discovery in learning relevant parts of thegraph, and developing validation tests that can better detect errors, both foraverage and conditional treatment effects. DoWhy is available athttps://github.com/microsoft/dowhy.

DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions